Genu valgum (GV) is a common lower limb deformity that may increase the risk of anterior cruciate ligament (ACL) injury. This study used OpenSim musculoskeletal modeling and kinematic analysis to investigate the mechanical responses of the ACL under fatigue in females with GV. Eight females with GV and eight healthy controls completed a running-induced fatigue protocol. Lower limb kinematic and kinetic data were collected and used to simulate stress and strain in the anteromedial ACL (A–ACL) and posterolateral ACL (P–ACL) bundles, as well as peak joint angles and knee joint stiffness. The results showed a significant interaction effect between group and fatigue condition on A–ACL stress. In the GV group, A–ACL stress was significantly higher than in the healthy group both before and after fatigue (p < 0.001) and further increased following fatigue (p < 0.001). In the pre-fatigued state, A–ACL strain was significantly higher during the late stance phase in the GV group (p = 0.036), while P–ACL strain significantly decreased post-fatigue (p = 0.005). Additionally, post-fatigue peak hip extension and knee flexion angles, as well as pre-fatigue knee abduction angles, showed significant differences between groups. Fatigue also led to substantial changes in knee flexion, adduction, abduction, and hip/knee external rotation angles within the GV group. Notably, knee joint stiffness in this group was significantly lower than in controls and decreased further post-fatigue. These findings suggest that the structural characteristics of GV, combined with exercise-induced fatigue, exacerbate A–ACL loading and compromise knee joint stability, indicating a higher risk of ACL injury in fatigued females with GV.

The kinematic and kinetic performance of tennis players differs across skill levels, with joint range of motion (ROM), moments, and stiffness being strongly linked to injury risk. Focusing on the biomechanical characteristics of lower-limb joints throughout the landing stage, especially among athletes of different skill levels, aids in understanding the link between injury risk and performance level. This study recruited 15 male campus tennis enthusiasts and 15 male professional tennis players. The kinematic and kinetic differences between amateur and professional players during the landing phase of the tennis serve were analyzed using SPM1D 0.4.11 and SPSS 27.0.1, with independent-sample t-tests applied in both cases. Throughout the tennis serve’s landing stage, the professional group exhibited significantly greater sagittal plane hip-joint stiffness (p < 0.001), horizontal plane moment (59~91%; p = 0.036), and a significantly higher peak moment (p = 0.029) in comparison with the amateur group. For the knee joint, the professional group exhibited significantly larger ROM in flexion–extension (0~82%; p = 0.003); along with greater ROM (0~29%; p = 0.042), moment (12~100%; p < 0.001), peak moment (p < 0.001) in adduction-abduction; and internal–external rotational moments (19~100%; p < 0.001) were markedly higher. The professional group showed significantly higher ankle joint ROM (p < 0.001) and moments (6~74%; p = 0.004) in the sagittal plane, as well as greater horizontal-plane ROM (27~67%; p = 0.041) and peak moments (p < 0.001). Compared with amateur tennis players, professional tennis players exhibit greater ROM, joint moments, and stiffness in specific planes, potentially increasing their risk of injury during the landing phase.

Footwear design, especially the curvature of carbon plates, may influence fatigue perception, but few studies have integrated footwear features into fatigue prediction models. This study aimed to develop a hybrid CNN-LSTM model to predict runners’ fatigue states and evaluate the impact of footwear characteristics on fatigue perception. Twelve male marathon runners (age = 21.8 ± 1.3 years; body mass = 59.1 ± 4.1 kg; height = 168.9 ± 2.2 cm; and weekly mileage = 68.8 ± 5.5 km) participated. They wore two types of carbon-plated shoes (flat plate, FP, and curved plate (CP)) and ran at a steady pace (Borg score 13) until a Borg score of 16 or 85% of maximum heart rate was reached for 2 min. EMG signals and physiological data were collected during treadmill running. A hybrid CNN-LSTM model was trained with and without footwear features to predict fatigue states. The model with footwear features achieved 85% accuracy, compared to 69% without. Curved carbon plate (CP) shoes delayed semi-fatigue onset, indicating better initial support, but the time to full fatigue was similar for both shoe types. The CNN-LSTM model effectively predicted fatigue states, with significant improvement when footwear features were included. Footwear design, particularly carbon plate curvature, influenced fatigue perception.